Understanding the importance of cybersecurity for industrial AI
Cybersecurity is a constant game of cat and mouse, as attackers discover new exploits and more advanced methods of compromising target systems while cybersecurity experts develop increasingly sophisticated means to repel them. Just as in many other industries, the advent of AI and generative AI has served to amplify what technology can achieve and, in the case of cybersecurity, this is true for both attackers and defenders.
Previously, the role of AI in educating users and managing threats was explored but, moving beyond that AI offers an unmatched ability to holistically monitor, manage and secure the entire foundation of a digital enterprise. But, for all the benefits AI offers, it must also be used responsibly to ensure it doesn’t create more security problems than it fixes. For any digital enterprise seeking to reap the benefits of AI, building a robust validation framework is also a must.
Building a secure AI backbone
Addressing cybersecurity concerns through individual bug fixes in software and user education are important elements in a companies overall cybersecurity plan, but managing and maintaining digital systems and configurations as a whole is an equally important step in the process. This is especially true for services that rely on external AI providers, where the AI model itself may change silently over time. Ensuring configurations are kept up to date and external AI services remain robust and reliable requires a new approach to cybersecurity.
Maintaining security-configuration baselines is an important part of any cybersecurity plan but it encompasses many manual processes that can be time consuming to keep track of while being too complex for traditional automation. As agentic AI continues to increase in capability, it will be perfectly positioned to take on many of the complex and important yet mundane tasks, automatically handling tasks like mapping rules between benchmark versions, categorizing rule updates and modifications of existing scripts to meet those requirements. Updating and validating that entire systems are in compliance with the latest regulations and patches is an important area that can often be difficult to stay on top of, resulting in gaps forming as updates are slow to be applied. Powerful AI automation will help ensure timely updates and secure systems through continuous monitoring and the ability to automatically generate code and update plans.
Large language models have the potential to be a great asset for cybersecurity and businesses as a whole, but very few will have the resources to develop and host these models completely in-house which means relying on external providers and AI labs. While this is, in and of itself is not bad, using LLMs without a framework in place to ensure robust, reliable and secure operation leaves a potential attack vector open to malicious actors. To maintain security, companies looking to implement LLM solutions must also take steps to validate any models that will be used, not just once, but continuously. This ensures not only that models haven’t been retrained at the source and that any information in transit also remains secure as well. Beyond that, ensuring a model is still fit for its intended use within a company is also a key step in in safe LLM implementation for crucial business tools.
As technology continues to advance, LLMs and cybersecurity will increasingly go hand in hand in industrial settings where security, reliability and explainability are all must-have elements of any new technology. AI and cybersecurity stand to gain a lot from each other, as AI helps support slow cybersecurity best practices while rigorous standards and tests ensure that AI itself is production ready. By combining the best of both worlds, not only will the digital enterprise become more secure, companies will be better able to take advantage of everything AI has to offer.
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